• Energy Research

Land use and land cover (LULC) robustly influence the delivery of the ecosystem services that humans rely on. This study used Kathmandu Valley as a study area which is a fast-growing and most vulnerable city to climate change. Remote sensing and GIS methods are the most significant methods for measuring the impact of LULC on the ecosystem service value (ESV). The satellite-based dataset was used for quantitative assessment of the LULC and ecosystem service value for 10-year intervals from the year 1989 to 2019. The result revealed that the area of forest cover, cropland, and waterbodies decreased by 28.33%, 4.35%, and 91.5%, respectively, whereas human settlement and shrubland increased by more than a hundred times and barren land by 21.14% at the end of the study period. This study found that Kathmandu valley lost 20.60% ESV over 30 years which dropped from USD 122.84 million to USD 97.54 million. The urban growth and extension of agricultural land to forest cover areas were found to be contributing factors for the reduction in ESV of Kathmandu valley. Cropland transformed into shrubland, bringing about an increase in ESV of some areas of the study region. In conclusion, the aggressive increase in population growth with inadequate urban planning and fragmentation of farmlands influenced the ESV of Kathmandu valley.

Land use and land cover (LULC) robustly influence the delivery of the ecosystem services that humans rely on. This study used Kathmandu Valley as a study area which is a fast-growing and most vulnerable city to climate change. Remote sensing and GIS methods are the most significant methods for measuring the impact of LULC on the ecosystem service value (ESV). The satellite-based dataset was used for quantitative assessment of the LULC and ecosystem service value for 10-year intervals from the year 1989 to 2019. The result revealed that the area of forest cover, cropland, and waterbodies decreased by 28.33%, 4.35%, and 91.5%, respectively, whereas human settlement and shrubland increased by more than a hundred times and barren land by 21.14% at the end of the study period. This study found that Kathmandu valley lost 20.60% ESV over 30 years which dropped from USD 122.84 million to USD 97.54 million. The urban growth and extension of agricultural land to forest cover areas were found to be contributing factors for the reduction in ESV of Kathmandu valley. Cropland transformed into shrubland, bringing about an increase in ESV of some areas of the study region. In conclusion, the aggressive increase in population growth with inadequate urban planning and fragmentation of farmlands influenced the ESV of Kathmandu valley.

Due to rising demand, the worldwide cement market is expected to increase from $340.61 billion in 2022 to $481.73 billion by 2029. Quarrying, raw material processing, and calcination are steps in cement production. The societies in India and Nepal have to deal with environmental issues such as air pollution, resource depletion, and the effects of climate change. A case study of Nepal's Udayapur Cement Industry Limited (UCIL) exposed antiquated production methods that reduce energy efficiency. Utilizing regression models like Extra Trees (Extremely Randomized Trees) Regressor, CatBoost (Categorial Boosting) Regressor, and XGBoost (eXtreme Gradient Boosting) Regressor, Random Forest and Ensemble of Sparse Embedded Trees (SET) machine learning is used to examine the demand, supply, and Gross Domestic Product (GDP) performance of cement manufacturing in India which shares a common cement related infrastructure to Nepal. Since businesses understand how important sustainability is to attract new customers and minimizing environmental effects, our study emphasizes the necessity of sustainable practices in the cement production industry. On evaluation, the Extra Trees Regressor showed strong performance, along with the SET (Stacking) model, which was further validated using a nested cross-validation technique. Random Forest, on the other hand, had trouble; it displayed the greatest RMSE (15617.85) and the lowest testing (0.8117), suggesting poorer generalization. The SET (Stacking) Ensemble model gained a testing R2 score (0.9372) and a testing RMSE (9019.76). In cross-validation, the Extra Trees model with a mean cross-validation R2 score of 0.93 and a low standard deviation of 0.04 proved to be the best-performing model, as evidenced by lower differences in R2 score across folds compared to other models, demonstrating its high predictive performance. The SHAP (SHapley Additive exPlanations) interpretability analysis indicates that population is the primary factor influencing GDP estimates. A Tkinter-based application was also developed to forecast GDP using the training model. To attain sustainability and lessen the effects of climate change on the cement sector, these findings highlight the adoption of cutting-edge technologies and energy-efficient procedures.

Due to rising demand, the worldwide cement market is expected to increase from $340.61 billion in 2022 to $481.73 billion by 2029. Quarrying, raw material processing, and calcination are steps in cement production. The societies in India and Nepal have to deal with environmental issues such as air pollution, resource depletion, and the effects of climate change. A case study of Nepal's Udayapur Cement Industry Limited (UCIL) exposed antiquated production methods that reduce energy efficiency. Utilizing regression models like Extra Trees (Extremely Randomized Trees) Regressor, CatBoost (Categorial Boosting) Regressor, and XGBoost (eXtreme Gradient Boosting) Regressor, Random Forest and Ensemble of Sparse Embedded Trees (SET) machine learning is used to examine the demand, supply, and Gross Domestic Product (GDP) performance of cement manufacturing in India which shares a common cement related infrastructure to Nepal. Since businesses understand how important sustainability is to attract new customers and minimizing environmental effects, our study emphasizes the necessity of sustainable practices in the cement production industry. On evaluation, the Extra Trees Regressor showed strong performance, along with the SET (Stacking) model, which was further validated using a nested cross-validation technique. Random Forest, on the other hand, had trouble; it displayed the greatest RMSE (15617.85) and the lowest testing (0.8117), suggesting poorer generalization. The SET (Stacking) Ensemble model gained a testing R2 score (0.9372) and a testing RMSE (9019.76). In cross-validation, the Extra Trees model with a mean cross-validation R2 score of 0.93 and a low standard deviation of 0.04 proved to be the best-performing model, as evidenced by lower differences in R2 score across folds compared to other models, demonstrating its high predictive performance. The SHAP (SHapley Additive exPlanations) interpretability analysis indicates that population is the primary factor influencing GDP estimates. A Tkinter-based application was also developed to forecast GDP using the training model. To attain sustainability and lessen the effects of climate change on the cement sector, these findings highlight the adoption of cutting-edge technologies and energy-efficient procedures.

In this study, the life cycle assessment (LCA) method has been used to evaluate the environmental impacts of various municipal solid waste (MSW) management system scenarios in Banepa municipality, Nepal, in terms of global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), human toxicity potential (HTP), abiotic depletion potential (ADP), and photochemical ozone creation potential (POCP). There are at least six possible scenarios of MSW management in Banepa: the current or baseline scenario (Scenario 1); composting with landfilling (Scenario 2); material recovery facility (MRF) recycling, composting, and landfilling (Scenario 3); MRF and anaerobic digestion (AD); composting, and landfilling (Scenario 4); MRF, composting, AD, and landfilling (Scenario 5); and, finally, incineration with landfilling (Scenario 6). Using both information from Ecoinvent 3.6 (2019) and published research articles, a spreadsheet tool based on the LCA approach was created. The impact of the recycling rate on each of the six abovementioned scenarios was evaluated using sensitivity analysis, which showed that the recycling rate can considerably decrease the life-cycle emissions from the MSW management system. Scenario 3 was found to have the least overall environmental impact with a GWP of 974.82 kg CO2 eq. per metric ton (t), EP of 0.04 kg PO4 eq./t, AP of 0.15 kg SO2 eq./t, HTP of 4.55 kg 1,4 DB eq./t, ADP of −0.03 kg Sb eq./t, and POCP of 0.06 kg C2H4 eq./t. By adoption of MRF and biological treatments such as composting and AD, environmental impact categories such as AP, EP, HTP, ADP, POCP, and GWP can be significantly reduced. The findings of this study can potentially serve as a reference for cities in the developing world in order to aid in both the planning and the operation of environmentally friendly MSW management systems.

In this study, the life cycle assessment (LCA) method has been used to evaluate the environmental impacts of various municipal solid waste (MSW) management system scenarios in Banepa municipality, Nepal, in terms of global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), human toxicity potential (HTP), abiotic depletion potential (ADP), and photochemical ozone creation potential (POCP). There are at least six possible scenarios of MSW management in Banepa: the current or baseline scenario (Scenario 1); composting with landfilling (Scenario 2); material recovery facility (MRF) recycling, composting, and landfilling (Scenario 3); MRF and anaerobic digestion (AD); composting, and landfilling (Scenario 4); MRF, composting, AD, and landfilling (Scenario 5); and, finally, incineration with landfilling (Scenario 6). Using both information from Ecoinvent 3.6 (2019) and published research articles, a spreadsheet tool based on the LCA approach was created. The impact of the recycling rate on each of the six abovementioned scenarios was evaluated using sensitivity analysis, which showed that the recycling rate can considerably decrease the life-cycle emissions from the MSW management system. Scenario 3 was found to have the least overall environmental impact with a GWP of 974.82 kg CO2 eq. per metric ton (t), EP of 0.04 kg PO4 eq./t, AP of 0.15 kg SO2 eq./t, HTP of 4.55 kg 1,4 DB eq./t, ADP of −0.03 kg Sb eq./t, and POCP of 0.06 kg C2H4 eq./t. By adoption of MRF and biological treatments such as composting and AD, environmental impact categories such as AP, EP, HTP, ADP, POCP, and GWP can be significantly reduced. The findings of this study can potentially serve as a reference for cities in the developing world in order to aid in both the planning and the operation of environmentally friendly MSW management systems.